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An arrhythmia classification using a deep learning and optimisation-based methodology

Journal of Medical engineering and Technology 2024年第7期48卷 253-261页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal India

The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals are segmented by R peak detection process. Thereafter, the greyscale and scalograms images have been formed. The features of the images are extracted using the EfficientNet-B0 deep learning model. These features are normalised using z-score normalisation method and then optimal features are selected using the hybrid feature selection method. The hybrid feature selection is constructed utilising two filter methods and Self Adaptive Bald Eagle Search (SABES) optimisation algorithm. The proposed methodology has been applied to the ECG signals for the classification of the five types of beats. The methodology acquired 99.31% of accuracy. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Contrastive Learning

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

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Digital Communications and Networks 2024年第4期10卷 904-917页

作者： Arif Ullah Ziaul Haq Abbas Ghulam Abbas Fazal Muhammad Jae-Mo Kang Department of Computer Engineering College of IT ConvergenceChosun UniversityGwangju61452South Korea Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Artificial Intelligence Kyungpook National UniversityDaegu41566South Korea

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data *** consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)*** user centric deployment of mmWave SBSs inevitably incurs correlation between UE and *** a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave *** using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power *** UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy *** also provide Monte Carlo simulation results to validate the accuracy of the derived ***,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave *** results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

关键词： Downlink cell association Heterogeneous cellular networks Integrated sub-6GHz and mmWave networks Millimeter wave communications Poisson cluster process

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Privacy Comparison for Bitcoin Light Client Implementations 6

Privacy Comparison for Bitcoin Light Client Implementations

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6th Conference on Advances in Financial Technologies, AFT 2024

作者： Kotzer, Arad Rottenstreich, Ori The Department of Computer Science Technion Haifa Israel The Department of Computer Science The Department of Electrical and Computer Engineering Technion Haifa Israel

ISBN: (纸本)9783959773454

Light clients implement a simple solution for Bitcoin’s scalability problem, as they do not store the entire blockchain but only the state of particular addresses of interest. To be able to keep track of the updated state of their addresses, light clients rely on full nodes to provide them with the required information. To do so, they must reveal information about the addresses they are interested in. This paper studies the two most common light client implementations, SPV and Neutrino with regards to their privacy. We define privacy metrics for comparing the privacy of the different implementations. We evaluate and compare the privacy of the implementations over time on real Bitcoin data and discuss the inherent privacy-communication tradeoff. In addition, we propose general techniques to enhance light client privacy in the existing implementations. Finally, we propose a new SPV-based light client model, the aggregation model, evaluate it, and show it can achieve enhanced privacy than in the existing light client implementations. © Arad Kotzer and Ori Rottenstreich.

关键词： Differential privacy

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Agricultural supply chain management using hyperledger and AIOT

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Journal of Ambient Intelligence and Humanized Computing 2025年第4期16卷 471-485页

作者： Jha, Anurag Kumar Raj, Aparna Jha, Ashish Kumar Shetty, Sujala D. Department of Computer Science Bits-Pilani Dubai Campus Dubai United Arab Emirates Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

Supply chain management and Hyperledger are two interconnected domains. They leverage blockchain technology to enhance efficiency, transparency, and security in supply chain operations. Together, they provide a decentralized, traceable, and real-time platform for recording and managing transactions. This combination is particularly valuable for industries dealing with sensitive goods, as it provides accurate traceability and real-time information. This paper explores the integration of supply chain management with Hyperledger blockchain technology to enhance efficiency, transparency, and security in supply chain operations. We propose a decentralized Hyperledger Fabric blockchain network to improve traceability, security, and efficiency by monitoring environmental conditions. This approach is particularly beneficial for transporting sensitive goods, such as medical supplies and perishable items, by ensuring optimal conditions and real-time data accessibility. The integration of Artificial Intelligence (AI) further enhances insights, reduces waste, and improves overall efficiency. By utilizing a distributed network free from third-party intermediaries, the system ensures immutability and remote accessibility, addressing challenges related to transporting heat and humidity sensitive products. Our experimental assessment demonstrates the benefits of private blockchain technologies, including enhanced security, regulatory compliance, compatibility, flexibility, and scalability. This study presents a detailed methodology for developing a traceable, efficient, and sustainable agricultural supply chain. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Efficiency

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Consensus-relevance kNN and covariate shift mitigation

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MACHINE LEARNING 2024年第1期113卷 325-353页

作者： Kalpakis, Konstantinos Computer Science and Electrical Engineering Department University of Maryland Baltimore County 1000 Hilltop Circle Baltimore MD 21250 USA

Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature, and computational efficiency. Nevertheless, in existing kNN algorithms, the kNN radius, which plays a major role in the quality of kNN estimates, is independent of any weights associated with the training samples in a kNN-neighborhood. This omission, besides limiting the performance and flexibility of kNN, causes difficulties in correcting for covariate shift (e.g., selection bias) in the training data, taking advantage of unlabeled data, domain adaptation and transfer learning. We propose a new weighted kNN algorithm that, given training samples, each associated with two weights, called consensus and relevance (which may depend on the query on hand as well), and a request for an estimate of the posterior at a query, works as follows. First, it determines the kNN neighborhood as the training samples within the kth relevance-weighted order statistic of the distances of the training samples from the query. Second, it uses the training samples in this neighborhood to produce the desired estimate of the posterior (output label or value) via consensus-weighted aggregation as in existing kNN rules. Furthermore, we show that kNN algorithms are affected by covariate shift, and that the commonly used sample reweighing technique does not correct covariate shift in existing kNN algorithms. We then show how to mitigate covariate shift in kNN decision rules by using instead our proposed consensus-relevance kNN algorithm with relevance weights determined by the amount of covariate shift (e.g., the ratio of sample probability densities before and after the shift). Finally, we provide experimental results, using 197 real datasets, demonstrating that the proposed approach is slightly better (in terms of F-1 score) on average than competing benchmark approaches for mit

关键词： Nearest neighbors Selection bias Covariate shift Density estimation kNN learners

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A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

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computers, Materials & Continua 2023年第8期76卷 1385-1408页

作者： Yongcheng Cui Guohui Tian Xiaochun Cheng School of Control Science and Engineering Shandong UniversityJinanChina Computer Science Department Swansea UniversityWalesUK

Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task *** the field matures,various state-of-the-art IS architectures have been *** of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service *** this end,we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture(SOA).Specifically,we first use the distributed deployment of functional modules to solve the problem of high computing resource ***,the Socket communication interface layer is designed to improve the calling efficiency of the function ***,the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing ***,we design and deploy an interactive system based on Browser/Server(B/S)architecture,which aims to display the status of the robot in real-time as well as to expand and call the robot *** system is integrated into the private cloud framework,which provides a feasible solution for improving the quality of ***,it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right *** results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service *** is an efficient way to transmit data and reduce the computational burden on the *** using our cloud detection module,the robot system can save approximately 25% of the averageCPUusage and reduce the average detection time by 0.1 s compared to the locally deployed system,demonstrating the reliability and practicality of our proposed architecture.

关键词： Service robot intelligent space cloud robot interactive robot system robot cloud service framework

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Deep learning and vision transformers-based framework for breast cancer and subtype identification

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Neural Computing and Applications 2025年第16期37卷 9311-9330页

作者： Jahan, Ishrat Chowdhury, Muhammad E. H. Vranic, Semir Al Saady, Rafif Mahmood Kabir, Saidul Pranto, Zahid Hasan Mim, Sabiha Jahan Nobi, Sadia Farhana Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Department of Electrical Engineering Qatar University 2713 Doha Qatar College of Medicine QU Health Qatar University Doha Qatar Department of Electrical and Electronic Engineering University of Dhaka Dhaka1000 Bangladesh

Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer det

关键词： Diseases

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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SUMIT: Secure Unicast and Multicast Communication in Internet of Mobile Things

SUMIT: Secure Unicast and Multicast Communication in Interne...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Reefat, Hasin Ishraq Ebrahimabadi, Mohammad Younis, Mohamed Alkanhal, Mona Karimi, Naghmeh Department of Computer Science and Electrical Engineering United States

ISBN: (纸本)9798350351255

An Internet of Mobile Things (IoMT) refers to an internetworked group of pervasive devices that coordinate their motion and task execution through frequent status and data exchange. An IoMT could be serving critical applications such as military reconnaissance, security surveillance, etc., and hence the authenticity, integrity and confidentiality of the transmitted data must be ensured. Yet, achieving the security goals is challenging due to the dynamic nature of the network topology, the constrained computational and communication resources, and the variety of packet traffic patterns among the nodes. This paper proposes an effective solution that leverages lightweight hardware primitives, specifically, Physical Unclonable Functions (PUFs), to support secure communication in the network. The employed PUFs are used to generate peer-to-peer encryption keys to protect the data traffic among nodes and to the command center, while coping with the dynamic change of the network topology. Our solution efficiently supports both unicast and multicast communications. The proposed solution is validated through analysis and prototype implementation on an FPGA. © 2024 IEEE.

关键词： Secure communication

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